After this, as in [1]}, we feed an embedded representation of \(t_t\)  into an LSTM to compute the hidden and cell state for each of its children. We always train using teacher forcing (passing the true value of \(t_t\)  into the LSTM rather than the generated value) because a single incorrect token that generates different categories of children than the correct token could make the probability of generating a correct token for any of its children zero. This could zero out most of our gradients, slowing down training.
